Sales forecasting using classical and machine learning approaches – A comparative study

This research makes a comparison of sales predictions from classical statistical models with predictions from machine learning and deep learning models. Since sales forecasting is an activity that has an integral impact on an organization, the need to establish more accurate forecasting methods has prompted a large number of studies on the subject, in turn driving the creation of new models, however, one issue that was displaced is the error metrics of these predictions and their impact on the business sector. Ten different forecasting models were used to obtain the predictions of four datasets, and their accuracy was measured using classical metrics such as ratio coefficients and vertical error metrics, and new theories such as peak similarity were also considered to establish the horizontal error of the predictions. The results show that classical statistical models showed the best metrics and coefficients, and that the choice of the best prediction model is not necessarily consistent with the best results for each metric. These results demonstrate the existence of under-studied error metrics with results that have an impact on the business world and may affect the choice of forecasting models.